Nonlinear Processes in Geophysics www.nonlin-processes-geophys.net 1023-5809 1607-7946 15 6 2008 10.5194/npg-15-863-2008 http://www.nonlin-processes-geophys.net/15/863/2008/ http://www.nonlin-processes-geophys.net/15/863/2008/npg-15-863-2008.html http://www.nonlin-processes-geophys.net/15/863/2008/npg-15-863-2008.pdf 863 871 2008-11-21 Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier H. Hashemi D. M. J. Tax R. P. W. Duin A. Javaherian javaheri@ut.ac.ir P. de Groot Institute of Geophysics, University of Tehran, P.O. Box 14155-6466, Tehran, I. R. Iran ICT, Faculty of EEMCS, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands dGB Earth Sciences BV, Nijverheidstraat 11-2, 7511 JM, Enschede, The Netherlands Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA). In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP) and support vector classifier (SVC) are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers. Aminzadeh, F. and Connolly, D.: Looking for gas chimneys and faults, AAPG Explorer, 23(12), 20–21, 2002. Aminzadeh, F. and de Groot, P.: Neural networks and other soft computing techniques with applications in the oil industry, EAGE Publications BV, Houten, 161 pp., 2006. Chopra, S. and Marfurt, K. J.: Seismic attributes: A new historical perspective, Geophysics, 70(4), 3SO–28SO, 2005. Connolly, D. L., Aminzadeh, F., de Groot, P., Ligtenberg, H., and Sawyer, R.: Gas chimney processing as a new exploration tool: A west Africa example, Proc. of AAPG Annual meeting, Houston, 10–13 March 2002. Corres, C. and Varpnik, V.: Support vector networks, Mach. Lear., 20(2), 165–194, 1995. Friedman, J. H.: Regularized discriminant analysis, J. Am. Stat. Assoc., 84, 165–175, 1989. Heggland, R., Meldahl, P., Bril, B., and de Groot, P.: The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part 2: Interpretation, SEG, Houston, Expanded Abstracts v. 1, 935–940, 1999. Heggland, R., Meldahl, P., de Groot, P., and Aminzadeh, F.: Seismic chimney interpretation examples from the North Sea and the Gulf of Mexico, American Oil & Gas Reporter, February issue, 78–83, 2000. Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are universal approximators, Neural Networks, 2(4), 359–366, 1989. Jang, J. S. R., Sun, C. T., and Mizutani, E.: Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence, Pearson Education Ltd., Delhi, 614 pp., 2005. Kecman, V., Arthanari, T., and Hadzic, I.: LP and QP based learning from empirical data, IEEE Proceedings of IJCNN, 4, 2451–2455, 2001. Kuncheva, L. I.: Combining pattern classifiers: methods and algorithms, John Wiley & Sons Inc., Hoboken, New Jersey, 374 pp., 2004. Lees, B. G.: Neural network applications in the geosciences: an introduction, Computers and Geosciences, 22(9), 955–957, 1996. Ligtenberg, J. H. and Thomsen, R. O.: Fluid migration path detection and its application to basin modeling, EAGE 65th conference, Stavanger, Expanded Abstract, C27, 4 pp., 2003. Meldahl, P., Heggland, R., Bril, B. and de Groot, P.: The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part 1: methodology, Annual Meeting, SEG, Houston, Expanded Abstracts v 1, 931–934, 1999. Meldahl, P., Heggland, R., Bril, B., and de Groot, P.: Identifying faults and gas chimneys using multiattributes and neural networks, The Leading Edge, 20, 474–482, May 2001. Platt, J. C., Smola, A., Bartlett, P., Scholkopf, B., and Schuurmans, D.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, advances in large margin classifiers, MIT Press, Cambridge, 61–74, 1999. Rosenblatt, E.: The Perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Rev., 65(6), 386–408, 1958. Taner, M. T., Schuelke, J. S., O'Doherty, R., and Baysal, E.: Seismic attributes revisited: 64th Annual International Meeting, SEG, Los Angeles, 1104–1106, 1994. Tingdahl, K. M., Bril, A. H., and de Groot, P. F.: Improving seismic chimney detection using directional attributes, J Petrol. Sci. Eng., 29, 205–211, 2001. Schroot, B. M., Klaver, G. T., and Schuttenhelm, R. T. E.: Surface and subsurface expression of gas seepage to the seabed-examples from the southern North Sea, Mar. Petrol. Geol., 22(3), 499–515, 2005. van der Baan, M. and Jutten, C.: Neural networks in geophysical applications, Geophysics, 65, 1032–1047, 2000. van der He\bijden, F., Duin, R. P. W., de Ridder, D., and Tax., D. M. J.: Classification, parameter estimation and estate estimation: an engineering approach using Matlab, John Willey & Sons Inc., West Sussex, 2004. Varpnik, V.: The nature of statistical learning theory, Springer-Verlag, New York, 314 pp., 1995.